As computing devices become progressively sophisticated and the Internet is readily accessible to more and more users, the creation, editing, and consumption of digital video has proliferated. Applications such as video-on-demand, video-sharing, digital video broadcasting, massive open online courses (MOOCs) or distance education, among other uses of digital video, are increasingly popular and have created a need to efficiently describe, organize, and manage video data. A conventional approach is to manually segment a video to generate an index or description of the content of the video. However, such an approach is time-intensive and prohibitively costly. Further, manual video segmentation is highly subjective because it requires a person to make his own judgments on how to index the video. In addition, when a digital video has been manually indexed, the video segmentation can be inconsistent, inaccurate, and/or incomplete.